Methods for occlusion handling have been proposed [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [3]}, [8]}, [9]}.
A layout consistent random field is used in [2]} to segment images of cars and faces by imposing asymmetric local spatial constraints.
Ghiasi et al. [11]} model occlusion by learning deformable models with local templates for human pose estimation while [12]} reconstructs dense 3D shape for vehicle pose.
Tighe et al. [13]} build a histogram to predict occlusion overlap scores between two classes for inferring occlusion order in the scene parsing task.
Chen et al. [14]} handle occlusion by incorporating category specific reasoning and exemplar-based shape prediction for instance segmentation.
For pedestrian detection with occlusion, bi-box regression is proposed in [15]} for both full body and visible part estimation while repulsion loss [16]} and aggregation loss [17]} are designed to improve the detection accuracy.
SeGAN [18]} learns occlusion patterns by segmenting and generating the invisible part of an object.
Recently, OCFusion [19]} uses an additional branch to model instances fusion process for replacing detection confidence in panoptic segmentation.
A self-supervised scene de-occlusion method is proposed in [20]} by recovering the occlusion ordering and completing the mask and content for the invisible object parts.
